Compressed Sensing Image Reconstruction Based on Convolutional Neural Network

Abstract

Compressed sensing theory is widely used in image and video signal processing because of its low coding complexity, resource saving, and strong anti-interference ability. Although the compression sensing theory solves the problems brought by the traditional signal processing methods to a certain extent, it also encounters some new problems: the reconstruction time is long and the algorithm complexity is high. In order to solve these problems and further improve the quality of image processing, a new convolutional neural network structure CombNet is proposed, which uses the measured value of compression sensing as the input of the convolutional neural network, and connects a complete connection layer to get the final Output. Experiments show that CombNet has lower complexity and better recovery performance. At the same sampling rate, the peak signal-to-noise ratio (PSNR) is 12.79%–52.67% higher than Tval3 PSNR, 16.31%–158.37% higher than D-AMP, 1.00%–3.79% higher than DR2-Net, and 0.06%–2.60% higher than FCMN. It still has good visual appeal when the sampling rate is very low (0.01).

Keywords

• Image reconstruction
• Compressed sensing
• CNN
• Reconstruction accuracy
• PSNR